Statistical and Non-linear Dynamics Methods of Earthquake Forecast: Application in the Caucasus - Chelidze, Tamaz; Melikadze, Giorgi; Kiria, Tengiz; Jimsheladze, Tamar; Kobzev, Gennady (Frontiers in Earth Science, 2020)
In 20 th century more than 10 strong earthquakes (EQ) of magnitude from 6 to 7 hit the South Caucasus, causing thousands of casualties and gross economic losses. Thus, the problem of strong EQs forecast is an actual problem for the region. In this direction, we developed the physical percolation model of fracture, which considers the final failure of solid as a termination of prolonged process of destruction -generation and clustering of micro-cracks -till appearance, at some critical concentration, of the infinite cluster, marking the final failure. Percolation provides a model of preparation of an individual strong event (slip, earthquake). The natural seismic process contains many such events: the appropriate model is a non-linear stick-slip model, which is a particular case of the general theory of integrate-and-fire process. Nonlinearity of seismic process is in contradiction with a memory-less Poissonian approach to seismic hazard. The complexity theory offers a chance to improve strong earthquakes' forecast using analysis of hidden (nonlinear) patterns in seismic time series, such as attractors in the phase space plot.For a regional forecast we applied Bayesian approach to assess conditional probability of expected in the next 5 years strong EQ of magnitude 5 and more. Later on, in addition to Bayesian probability, we applied the pattern recognition technique to seismic time series, based on the assessment of empirical risk function (Generalized Portrait method): nowadays this approach is known as Support Vector Machines (SVM) technique. The preliminary analysis shows that application of GP technique predicts retrospectively 80% of M5 events in Caucasus.Besides regional forecast studies, intensive work is under way on short-term EQ prediction also. Here we present the results of multi-parametrical (hydrodynamic and magnetic) monitoring carried out in 2017-2019 on the territory of Georgia. In order to assess the reliability of the precursors, we used machine learning approach, namely the algorithm of deep learning ADAM, which optimizes target function by combination of optimization algorithm designed for neural networks and a method of stochastic gradient descent with momentum. Finally, we used the method of Receiver Operating Characteristics (ROC) to assess the forecast quality of this binary classifier system.